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Predicting Structured Data

MPS-Authors
/persons/resource/persons83791

Bakir,  GH
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://mitpress.mit.edu/books/predicting-structured-data
(Abstract)

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Citation

Bakir, G., Hofmann, T., Schölkopf, B., Smola, A., Taskar, B., & Vishwanathan, S. (2007). Predicting Structured Data. Cambridge, MA, USA: MIT Press.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CBAD-7
Abstract
Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional constraints found in structured data, poses one of machine learning’s greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field. The contributors discuss applications as diverse as machine translation, document markup, computational biology, and information extraction, among others, providing a timely overview of an exciting field.